BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D Models (2108.00516v1)

Abstract: Tracking the 6D pose of objects in video sequences is important for robot manipulation. Most prior efforts, however, often assume that the target object's CAD model, at least at a category-level, is available for offline training or during online template matching. This work proposes BundleTrack, a general framework for 6D pose tracking of novel objects, which does not depend upon 3D models, either at the instance or category-level. It leverages the complementary attributes of recent advances in deep learning for segmentation and robust feature extraction, as well as memory-augmented pose graph optimization for spatiotemporal consistency. This enables long-term, low-drift tracking under various challenging scenarios, including significant occlusions and object motions. Comprehensive experiments given two public benchmarks demonstrate that the proposed approach significantly outperforms state-of-art, category-level 6D tracking or dynamic SLAM methods. When compared against state-of-art methods that rely on an object instance CAD model, comparable performance is achieved, despite the proposed method's reduced information requirements. An efficient implementation in CUDA provides a real-time performance of 10Hz for the entire framework. Code is available at: https://github.com/wenbowen123/BundleTrack

• The paper introduces BundleTrack, a model-free approach for 6D pose tracking of novel objects without relying on pre-trained 3D models.
• It utilizes an RGB-D based optimization framework that iteratively refines pose estimates by accounting for uncertainty in dynamic environments.
• Experimental results show competitive accuracy and speed compared to state-of-the-art methods, enhancing robotic perception in real-world applications.

BundleTrack: 6D Pose Tracking for Novel Objects Without Instance or Category-Level 3D Models

The paper presents "BundleTrack," an innovative approach for 6D pose tracking of novel objects without the need for predefined 3D models, whether instance- or category-level. This research addresses a significant challenge in robotic perception, where traditional methods rely heavily on pre-existing 3D model datasets, limiting their applicability in dynamic and unstructured environments.

Core Contributions

The authors introduce a methodology that leverages a combination of RGB-D data and an optimization framework to track the 6D pose of objects. The primary contributions of this paper include:

1. Model-Free Approach: The developed system does not rely on any pre-trained models specific to the object instances or categories, which distinguishes it from prior works that necessitate large model databases.
2. Optimization Framework: The use of a novel optimization strategy allows for efficient pose estimation and tracking, which incorporates uncertainty estimation and refines predictions over time.
3. Evaluation and Results: Comprehensive experiments demonstrate the effectiveness of BundleTrack in a variety of settings. Specifically, the method is benchmarked against other state-of-the-art techniques, illustrating competitive performance even without prior model data. Numerical results highlight improvements in both accuracy and speed.

Methodological Insights

The paper explores the specific algorithms and techniques employed. At its core, the system utilizes an RGB-D camera setup, capturing both color and depth information. The processing pipeline includes:

• Feature Extraction: Identifying salient features in the scene that can be tracked across frames.
• Pose Initialization: Estimating an initial pose from the extracted features using geometric cues from the depth map.
• Iterative Optimization: Refining this estimate through optimization cycles, reducing error by accounting for object and environmental dynamics.

Implications and Future Directions

The implications of this research are substantial for various robotics applications, particularly in areas where encountering unknown objects is frequent, such as in domestic robots, manufacturing, and autonomous vehicles. By removing the dependency on pre-existing models, robotic systems could achieve higher levels of adaptability and efficiency in novel settings.

Theoretical development can be advanced by incorporating additional sensory data, such as tactile feedback or advanced vision techniques like stereo cameras, to improve robustness further. Additionally, exploring integration with machine learning frameworks could offer insights into enhancing the predictive capabilities of pose estimation, potentially reducing computational overhead while increasing accuracy.

Moreover, future work may aim to optimize the algorithm for real-time applications, a critical requirement for robotics tasks necessitating immediate responsiveness.

Conclusion

This paper contributes significantly to the field of robotic perception, providing a versatile framework for 6D pose tracking without the constraints of traditional model dependencies. Through rigorous experimentation and a robust methodological approach, it sets a foundation for further exploration into model-free pose estimation methods, ultimately enhancing the autonomy and adaptability of robotic systems.